Supplementary Material for: Salivary proteins and metabolites as Caries Biomarkers in adolescents

Introduction: The identification of salivary molecules that can be associated to dental caries could provide insights about caries risk and offer valuable information to develop caries prediction models. However, the search for a universal caries biomarker has proven elusive due to the multifactorial nature of this oral disease. We have therefore performed a systematic effort to identify caries-associated metabolites and proteins in saliva samples from adolescents that had a caries experience and those that were caries-free. Methods: Quantification of approximately 100 molecules was performed by the use of a wide range of techniques, ranging from NMR metabolomics to ELISA, Luminex or colorimetric assays, as well as clinical features like plaque accumulation and gingival index. In addition, simplified dietary and oral hygiene habits questionnaires were also obtained. Results: The caries-free group had significantly lower consumption of sweetened beverages and higher toothbrushing frequency. Surprisingly, very few compounds were found to individually provide discriminatory power between Caries-experienced and Caries-Free individuals. The data analysis revealed several potential reasons that could underly this lack of association value with caries, including differences in metabolite concentrations throughout the day, a lack of correlation between metabolite concentrations in plaque and saliva, or sex-related differences, among others. However, when multiple compounds were combined by multivariate analysis and random forest modelling, a combination of 3-5 compounds were found to provide good prediction models for morning (with an AUC accuracy of 0.87) and especially afternoon samples (AUC=0.93). Conclusion: While few salivary biomarker could differentiate between caries-free and caries-experienced adolescents, a combination of markers proved effective, particularcly in afternoon samples. To predict caries risk, these biomarkers should be validated in larger cohorts and longitudinal settings, considering factors such as gender differences, and variations in oral hygiene and diet.